si.Rmd

#quantile index column and column with quantile averages
#qidx: quantile index
#qavg_to_scale: column to scale
#first_target: target value of first quantile
#mean_target: target mean of scaled quantiles
scaled_quantiles <- function(.data, 
                          qidx, 
                          qavg_to_scale, 
                          first_target, 
                          mean_target) {
  
  # cumbersomely extract current quantile mean
  mean_current = .data %>%
    ungroup() %>%
    summarise(mean_cur = first(mean({{qavg_to_scale}}))) %>%
    pull(mean_cur)
    
  # cumbersomely extract current first wuantile value
  first_current = .data %>%
    ungroup() %>%
    arrange({{qidx}}) %>%
    summarise(first_cur = first({{qavg_to_scale}})) %>%
    pull(first_cur)
  
  df_tmp = .data %>%
    mutate(tmp = {{qavg_to_scale}}*mean_target/mean_current)
  
  first_tmp = df_tmp$tmp[1]
  
  df_tmp = df_tmp %>%
    mutate(scaled = mean_target-(mean_target-tmp) * (mean_target-first_target)/(mean_target-first_tmp)) %>%
    select({{qidx}}, scaled) %>%
    mutate(v_mean = mean_target, v_first = first_target)
}

## run once to save file
df_all = NULL
for (min_energy in seq(from=mer[1], to=mer[2], by=0.25)) {
  for (mean_energy in seq(from=mea[1], to=mea[2], by=0.25)) {
    if (min_energy <= mean_energy) {
      df_all = df_all %>%
      bind_rows(df_energy_deciles %>%
                  scaled_quantiles(eu_q_rank, pc_energy_use_gj, min_energy, mean_energy))
    }
  }
}

saveRDS(df_all, here("analysis/data/derived/si/scenarios_fine.rds"))
```

```{r , fig.width=7, fig.height=5.5, fig.cap="**Figure S6:**"}

round_by = 10

df_all = readRDS(here("analysis/data/derived/si/scenarios_fine.rds")) %>%
  filter(eu_q_rank %in% c(1,10)) %>%
  group_by(v_mean, v_first) %>%
  summarise(ratio = last(scaled)/first(scaled)) %>%
  mutate(bin_ratio = if_else((ratio*100)%%round_by > round_by*0.5, 
                             ratio*100+(round_by-(ratio*100)%%round_by),
                             ratio*100-(ratio*100)%%round_by)) %>%
  group_by(bin_ratio, v_first) %>%
  summarise(v_mean = mean(v_mean)) %>%
  mutate(bin_ratio = bin_ratio*0.01)

df_scenario = df_all %>%
  filter(v_mean %in% df_scenario_info$fe_gj_pc) %>%
  left_join(df_scenario_info, by=c("v_mean"="fe_gj_pc")) %>%
  filter(!(scenario == "DLE")) %>%
  mutate(scenario = dplyr::recode(scenario,
              "LED" = "LED (33)",
              "IEA ETP B2DS" = "IEA ETP B2DS (52)",
              "GEA efficiency" = "GEA efficiency (40)",
              "SSP1-1.9" = "SSP1-1.9 (54)",
              "SSP2-1.9" = "SSP2-1.9 (58)",
              "SSP4-1.9" = "SSP4-1.9 (64)",
              "SSP3-3.4" = "SSP3-3.4 (90)",
              "SSP5-6.0" = "SSP5-6.0 (94)"))

library(wesanderson)

a = df_all %>%
  ggplot(aes(x=v_first, y=bin_ratio, fill=v_mean)) +
  #geom_tile() +
  geom_hline(yintercept = ineq_curr, alpha=0.8, color="grey", linetype=2) +
  geom_line(data=df_scenario, aes(color=scenario, group=scenario)) +
  annotate(geom="text", x=max(df_all$v_first)-7.5,y=ineq_curr+0.6,label = "Current (2015) 10:10 ratio") +
  #scale_fill_viridis("Mean energy\navailable") +
  #scale_fill_gradient("Mean energy\navailable (GJ/cap)",
  #                    low=wes_palette("Chevalier1")[3], 
  #                    high = wes_palette("Rushmore1")[4]) +
  #scale_color_manual(values=wes_palette("Darjeeling1")) +
  theme_minimal() +
  labs(x="Minimum energy requirement (GJ/cap)", y="Maximum energy inequality (10:10 ratio)", color = "Scenario")+
  theme(text=element_text(family="Liberation Sans Narrow"),
        axis.text.x = element_text(size = 13),
        axis.text.y = element_text(size = 13)) +
  #scale_color_discrete(name = "Scenario (mean GJ/cap)") +
  xlim(10,53) +
  #ylim(0,15) +
  scale_y_continuous(breaks = c(2.5,5,7.5,10,12.5,15), limits = c(1,15)) +
  scale_color_discrete(name = "Scenario\n(mean GJ/cap)", breaks=c("LED (33)",
                               "GEA efficiency (40)",
                               "IEA ETP B2DS (52)",
                               "SSP1-1.9 (54)",
                               "SSP2-1.9 (58)",
                               "SSP4-1.9 (64)",
                               "SSP3-3.4 (90)",
                               "SSP5-6.0 (94)")) #+
  #theme_ipsum()
a
ggsave(here("analysis", "figures", "figureSxx.pdf"))
```

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# References 

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